We propose a face mosaicing approach to model both the facial appearance and geometry from pose-varying videos, and apply it in face tracking and recognition. The basic idea is that by approximating the human head as ...
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ISBN:
(纸本)0769523722
We propose a face mosaicing approach to model both the facial appearance and geometry from pose-varying videos, and apply it in face tracking and recognition. The basic idea is that by approximating the human head as a 3D ellipsoid, multi-view face images can be back projected onto the surface of the ellipsoid, and the surface texture map is decomposed into an array of local patches. During the online modeling process, the position and pose of the first frame is assumed to be known for a given video sequence. For each frame in the sequence, the algorithm estimates the face-position and pose, and generates a texture map, which is further utilized in updating the mosaic model.
Motivated by the success of free-parts based representations in face recognition, we have attempted to address some of the problems associated with applying such a philosophy to the task of speaker-independent visual ...
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Motivated by the success of free-parts based representations in face recognition, we have attempted to address some of the problems associated with applying such a philosophy to the task of speaker-independent visual speech recognition. A major problem with canonical area-based approaches in automatic visual speech recognition is the dependence these approaches have on locating and tracking the speaker’s region of interest (ROI) correctly. By employing a free-parts representation,we assume that the position/structure of patches within the mouth image can be relaxed so they can "freely" move to varying extents, hence reducing the influence of the front-end effect. In this paper, we show that by using a free-parts representation we gain some robustness against the problem of ROI localisation and tracking compared to current area-based feature extraction techniques such as the discrete cosine transform (DCT). Also in this paper, we expose the importance of representation for the task of visual speech recognition highlighted by the poor results current representations yield.
Pseudo-semantic labeling is a novel approach to organize mobile multimedia content such as images and videos. We have developed low-complexity algorithms to derive labels, such as "indoor/outdoor", "fac...
Pseudo-semantic labeling is a novel approach to organize mobile multimedia content such as images and videos. We have developed low-complexity algorithms to derive labels, such as "indoor/outdoor", "face/not face", that can be run on the mobile device. "Indoor/outdoor" classification is done based on the presence of sky in the images. Skin like pixels are detected based on the color information and if present they are matched to a pre-defined "facetemplate" to detect the presence of faces in images. We have developed an initial framework for finding the degree of blurriness in an image, which can be used for labels like "blurry/not blurry".
This paper presents a computer-assisted diagnostic system for mass detection and classification, which performs mass detection on regions of interest followed by the benign-malignant classification on detected masses....
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This paper presents a computer-assisted diagnostic system for mass detection and classification, which performs mass detection on regions of interest followed by the benign-malignant classification on detected masses. In order for mass detection to be effective, a sequence of preprocessing steps are designed to enhance the intensity of a region of interest, remove the noise effects and locate suspicious masses using five texture features generated from the spatial gray level difference matrix (SGLDM) and fractal dimension. Finally, a probabilistic neural network (PNN) coupled with entropic thresholding techniques is developed for mass extraction. Since the shapes of masses are crucial in classification between benignancy and malignancy, four shape features are further generated and joined with the five features previously used in mass detection to be implemented in another PNN for mass classification. To evaluate our designed system a data set collected in the Taichung Veteran General Hospital, Taiwan, R.O.C. was used for performance evaluation. The results are encouraging and have shown promise of our system.
Magnetic resonance imaging (MRI) intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly varying shading artifact...
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This paper proposes intensity and region-of-interest-based finite-element modeling of brain deformation. The modeling is based on the intensities of voxels within each element and motion analysis of region of interest...
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Texture is one of important features of masses in mammograms. A recent texture unit-based texture spectrum approach, referred to as Texture Unit Coding (TUC) has shown promise in texture classification. This paper pre...
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In this paper, we describe an adaptive lossless compression algorithm for color video sequences utilizing backward adaptive temporal prediction and an integer wavelet transform. We exploit two redundancies in color vi...
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Medical images usually require higher fidelity than commonly used natural images, especially with respect to detail preservation. Color medical images also require a higher degree of color preservation. In this paper,...
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Medical images usually require higher fidelity than commonly used natural images, especially with respect to detail preservation. Color medical images also require a higher degree of color preservation. In this paper, we present the extension of the novel gray-scale image compression technique, hybrid multi-scale vector quantization (HMVQ) to color image compression. Limitations of common color image compression methods in controlling bit-rate allocation for color channels are discussed, and the ability of HMVQ in overcoming such limitations while improving the color quality is demonstrated.
Hyperspectral imagery offers a means of uncovering enormous spectral information that can be used for various applications in data exploitation. How effectively such information is used affects the way image analysis ...
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